JLC 445 Project 2

1. PACKAGES

#install.packages('tidyverse')
#install.packages('leaflet')
#install.packages('sf')
#install.packages('tigris')
#install.packages('data.table')
library(tidyverse)
library(leaflet)
library(sf)
library(tigris)
library(data.table)

2. DATA

dc.data2023.temp <- fread("https://datagate.dc.gov/search/open/crimes?daterange=1-1-2023%20to%20date&details=true&format=csv")
dc.data2022 <- read.csv("https://opendata.arcgis.com/datasets/f9cc541fc8c04106a05a1a4f1e7e813c_4.csv", stringsAsFactors = FALSE)
dc.data2021 <- read.csv("https://opendata.arcgis.com/datasets/619c5bd17ca2411db0689bb0a211783c_3.csv", stringsAsFactors = FALSE)
dc.data2020 <- read.csv("https://opendata.arcgis.com/datasets/f516e0dd7b614b088ad781b0c4002331_2.csv", stringsAsFactors = FALSE)
dc.data2019 <- read.csv("https://opendata.arcgis.com/datasets/f08294e5286141c293e9202fcd3e8b57_1.csv", stringsAsFactors = FALSE)
dc.data2018 <- read.csv("https://opendata.arcgis.com/datasets/38ba41dd74354563bce28a359b59324e_0.csv", stringsAsFactors = FALSE)
dc.data2017 <- read.csv("https://opendata.arcgis.com/datasets/6af5cb8dc38e4bcbac8168b27ee104aa_38.csv", stringsAsFactors = FALSE)
dc.data2016 <- read.csv("https://opendata.arcgis.com/datasets/bda20763840448b58f8383bae800a843_26.csv", stringsAsFactors = FALSE)
dc.data2015 <- read.csv("https://opendata.arcgis.com/datasets/35034fcb3b36499c84c94c069ab1a966_27.csv", stringsAsFactors = FALSE)
dc.data2014 <- read.csv("https://opendata.arcgis.com/datasets/6eaf3e9713de44d3aa103622d51053b5_9.csv", stringsAsFactors = FALSE)
dc.data2013 <- read.csv("https://opendata.arcgis.com/datasets/5fa2e43557f7484d89aac9e1e76158c9_10.csv", stringsAsFactors = FALSE)
dc.data2012 <- read.csv("https://opendata.arcgis.com/datasets/010ac88c55b1409bb67c9270c8fc18b5_11.csv", stringsAsFactors = FALSE)
dc.data2011 <- read.csv("https://opendata.arcgis.com/datasets/9d5485ffae914c5f97047a7dd86e115b_35.csv", stringsAsFactors = FALSE)
dc.data2010 <- read.csv("https://opendata.arcgis.com/datasets/fdacfbdda7654e06a161352247d3a2f0_34.csv", stringsAsFactors = FALSE)
dc.data2009 <- read.csv("https://opendata.arcgis.com/datasets/73cd2f2858714cd1a7e2859f8e6e4de4_33.csv", stringsAsFactors = FALSE)
dc.data2008 <- read.csv("https://opendata.arcgis.com/datasets/180d56a1551c4e76ac2175e63dc0dce9_32.csv", stringsAsFactors = FALSE)

3. CLEAN DATA

dc.data.temp <- rbind(dc.data2008, dc.data2009, dc.data2010, dc.data2011, dc.data2012, dc.data2013, dc.data2014, dc.data2015, dc.data2016, dc.data2017, dc.data2018, dc.data2019, dc.data2020, dc.data2021, dc.data2022)
dc.data.temp <- separate(dc.data.temp, REPORT_DAT, into = c("DATE", "TIME"), sep = " ")
dc.data.temp <- dc.data.temp %>%
  select(3:9,12:22)
dc.data.temp$DATE <- as.Date(dc.data.temp$DATE, format = "%Y/%m/%d")
dc.data2023 <- separate(dc.data2023.temp, REPORT_DAT, into = c("DATE", "TIME"), sep = ", ")
dc.data2023 <- subset(dc.data2023, select = c("CCN", "DATE", "TIME", "SHIFT", "METHOD", "OFFENSE", "BLOCK", "WARD", "ANC", "DISTRICT", "PSA", "NEIGHBORHOOD_CLUSTER", "BLOCK_GROUP", "CENSUS_TRACT", "VOTING_PRECINCT", "LATITUDE", "LONGITUDE", "BID"))
dc.data2023$SHIFT <- toupper(dc.data2023$SHIFT)
dc.data2023$METHOD <- toupper(dc.data2023$METHOD)
dc.data2023$OFFENSE <- toupper(dc.data2023$OFFENSE)
dc.data2023$BLOCK <- toupper(dc.data2023$BLOCK)
dc.data2023$DATE <- as.Date(dc.data2023$DATE, format = "%m/%d/%Y")
dc.data <- rbind(dc.data.temp, dc.data2023)

4. ENRICH DATA

dc.data$YEAR <- substr(dc.data$DATE, 0, 4)
dc.data$MONTH <- month(dc.data$DATE)
dc.data$DAY <- day(dc.data$DATE)
dc.data$DOW <- weekdays(dc.data$DATE)
dc.data$HOUR <- substr(dc.data$TIME, 0, 2)

5. GEOGRAPHIC DATA

dc.roads <- roads("DC", "District of Columbia")
dc.roads.major <- dc.roads %>%  filter(RTTYP %in% c("I","S","U"))
dc.outline <- county_subdivisions("DC", "District of Columbia")

6.1 TRANSPARENT POINT MAP, THEFT/OTHER

data.dc.offense <- subset(dc.data, dc.data$OFFENSE == 'THEFT/OTHER')
ggplot() + 
  geom_sf(data = dc.outline) +
  geom_sf(data = dc.roads.major) +
  geom_point(aes(x = LONGITUDE, y = LATITUDE), data = data.dc.offense, alpha = 0.005, size = 0.5) + 
  theme(legend.position="bottom") +
  theme_void()

6.2 DENSITY/COUNTOUR MAP, BURGLARY

data.dc.offense <- subset(dc.data, dc.data$OFFENSE == 'BURGLARY')
ggplot() + 
  geom_sf(data = dc.outline) +
  stat_density2d(aes(x = LONGITUDE, y = LATITUDE, fill = after_stat(level), alpha = 0.01), linewidth = 0.001, bins = 10, data = data.dc.offense, geom = "polygon") + 
  geom_density2d(data = data.dc.offense, aes(x = LONGITUDE, y = LATITUDE), linewidth = 0.15)

6.3 INTERACTIVE CLUSTER MAP, ROBBERY

data.dc.offense <- subset(dc.data, dc.data$OFFENSE == 'ROBBERY')
leaflet(dc.data) %>%
  addTiles() %>%
  addMarkers(lng = ~LONGITUDE, lat = ~LATITUDE, popup = data.dc.offense, 
             clusterOptions = markerClusterOptions()) %>%
    addPolygons(data = dc.outline)

## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'